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SpydazWebAI_Examples.py

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+################################  language examples ##############################
+
+## LlamaTokenizerFast
+tokenizer = MistralTokenizerFast.from_pretrained("mistralai/Mistral-7B-v0.1")
+tokenizer.encode("Hello this is a test")
+
+################################ INITIALIZE Mistral MODEL ##############################
+## INITIALIZE MistralStarConfig
+
+# Initializing a Mistral 7B style configuration
+configuration = MistralStarConfig()
+
+# Initializing a model from the Mistral 7B style configuration
+model = MistralModel(configuration)
+
+# Accessing the model configuration
+configuration = model.config
+
+## INITIALIZE MistralStarConfig
+
+# Initializing a Mistral 7B style configuration
+configuration = MistralQuietConfig()
+
+# Initializing a model from the Mistral 7B style configuration
+model = MistralModel(configuration)
+
+# Accessing the model configuration
+configuration = model.config
+
+
+## INITIALIZE MODEL
+
+# Initializing a Mistral 7B style configuration
+configuration = MistralConfig()
+
+# Initializing a model from the Mistral 7B style configuration
+model = MistralModel(configuration)
+
+# Accessing the model configuration
+configuration = model.config
+
+
+## INITIALIZE MODEL-Examples
+
+# Download model and configuration from huggingface.co and cache.
+model = MistralModel.from_pretrained("mistralai/Mistral-7B-v0.1")
+# Model was saved using *save_pretrained('./test/saved_model/')* (for example purposes, not runnable).
+model = MistralModel.from_pretrained("./test/saved_model/")
+# Update configuration during loading.
+model = MistralModel.from_pretrained("mistralai/Mistral-7B-v0.1", output_attentions=True)
+assert model.config.output_attentions == True
+# Loading from a TF checkpoint file instead of a PyTorch model (slower, for example purposes, not runnable).
+config = MistralConfig.from_json_file("./tf_model/my_tf_model_config.json")
+model = MistralModel.from_pretrained("./tf_model/my_tf_checkpoint.ckpt.index", from_tf=True, config=config)
+# Loading from a Flax checkpoint file instead of a PyTorch model (slower)
+model = MistralModel.from_pretrained("mistralai/Mistral-7B-v0.1", from_flax=True)
+################################  MistralForCausalLM ##############################
+
+## MistralForCausalLM
+
+model = MistralForCausalLM.from_pretrained("mistralai/Mistral-7B-v0.1")
+tokenizer = MistralTokenizerFast.from_pretrained("mistralai/Mistral-7B-v0.1")
+
+prompt = "Hey, are you conscious? Can you talk to me?"
+inputs = tokenizer(prompt, return_tensors="pt")
+
+# Generate
+generate_ids = model.generate(inputs.input_ids, max_length=30)
+tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
+## "Hey, are you conscious? Can you talk to me?\nI'm not conscious, but I can talk to you."
+
+################################  MistralForSequenceClassification ##############################
+
+### MistralForSequenceClassification - single-label classification:
+
+tokenizer = MistralTokenizerFast.from_pretrained("mistralai/Mistral-7B-v0.1")
+model = MistralForSequenceClassification.from_pretrained("mistralai/Mistral-7B-v0.1")
+
+inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
+
+with torch.no_grad():
+    logits = model(**inputs).logits
+
+predicted_class_id = logits.argmax().item()
+
+# To train a model on `num_labels` classes, you can pass `num_labels=num_labels` to `.from_pretrained(...)`
+num_labels = len(model.config.id2label)
+model = MistralForSequenceClassification.from_pretrained("mistralai/Mistral-7B-v0.1", num_labels=num_labels)
+
+labels = torch.tensor([1])
+loss = model(**inputs, labels=labels).loss
+
+
+### MistralForSequenceClassification - multi-label classification:
+
+
+tokenizer = MistralTokenizerFast.from_pretrained("mistralai/Mistral-7B-v0.1")
+model = MistralForSequenceClassification.from_pretrained("mistralai/Mistral-7B-v0.1", problem_type="multi_label_classification")
+
+inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
+
+with torch.no_grad():
+    logits = model(**inputs).logits
+
+predicted_class_ids = torch.arange(0, logits.shape[-1])[torch.sigmoid(logits).squeeze(dim=0) > 0.5]
+
+# To train a model on `num_labels` classes, you can pass `num_labels=num_labels` to `.from_pretrained(...)`
+num_labels = len(model.config.id2label)
+model = MistralForSequenceClassification.from_pretrained(
+    "mistralai/Mistral-7B-v0.1", num_labels=num_labels, problem_type="multi_label_classification"
+)
+
+labels = torch.sum(
+    torch.nn.functional.one_hot(predicted_class_ids[None, :].clone(), num_classes=num_labels), dim=1
+).to(torch.float)
+loss = model(**inputs, labels=labels).loss
+
+################################  MistralForTokenClassification ##############################
+
+
+### MistralForTokenClassification
+
+tokenizer = MistralTokenizerFast.from_pretrained("mistralai/Mistral-7B-v0.1")
+model = MistralForTokenClassification.from_pretrained("mistralai/Mistral-7B-v0.1")
+
+inputs = tokenizer(
+    "HuggingFace is a company based in Paris and New York", add_special_tokens=False, return_tensors="pt"
+)
+
+with torch.no_grad():
+    logits = model(**inputs).logits
+
+predicted_token_class_ids = logits.argmax(-1)
+
+# Note that tokens are classified rather then input words which means that
+# there might be more predicted token classes than words.
+# Multiple token classes might account for the same word
+predicted_tokens_classes = [model.config.id2label[t.item()] for t in predicted_token_class_ids[0]]
+predicted_tokens_classes
+
+labels = predicted_token_class_ids
+loss = model(**inputs, labels=labels).loss
+round(loss.item(), 2)
+
+
+
+
+################################  MistralForQuestionAnswering ##############################
+
+
+tokenizer = MistralTokenizerFast.from_pretrained("mistralai/Mistral-7B-v0.1")
+model = MistralForQuestionAnswering.from_pretrained("mistralai/Mistral-7B-v0.1")
+
+question, text = "Who was Jim Henson?", "Jim Henson was a nice puppet"
+
+inputs = tokenizer(question, text, return_tensors="pt")
+with torch.no_grad():
+    outputs = model(**inputs)
+
+answer_start_index = outputs.start_logits.argmax()
+answer_end_index = outputs.end_logits.argmax()
+
+predict_answer_tokens = inputs.input_ids[0, answer_start_index : answer_end_index + 1]
+
+# target is "nice puppet"
+target_start_index = torch.tensor([14])
+target_end_index = torch.tensor([15])
+
+outputs = model(**inputs, start_positions=target_start_index, end_positions=target_end_index)
+loss = outputs.loss
+
+
+
+
+
+
+
+################################  Mixtral MOA  Models ##############################
+
+################################################################
+# Initializing a Mixtral 7B style configuration
+################################################################
+
+configuration = MixtralConfig()
+
+# Initializing a model from the Mixtral 7B style configuration
+model = MixtralModel(configuration)
+
+# Accessing the model configuration
+configuration = model.config
+
+################################################################
+### The base model can be used as follows:
+################################################################
+
+model = MixtralForCausalLM.from_pretrained("mistralai/Mixtral-8x7B-v0.1", device_map="auto")
+tokenizer = MistralTokenizerFast.from_pretrained("mistralai/Mixtral-8x7B-v0.1")
+
+prompt = "My favourite condiment is"
+
+model_inputs = tokenizer([prompt], return_tensors="pt").to("cuda")
+model.to("cpu")
+
+generated_ids = model.generate(**model_inputs, max_new_tokens=100, do_sample=True)
+tokenizer.batch_decode(generated_ids)[0]
+
+
+################################################################
+### The instruction tuned model can be used as follows:
+################################################################
+
+model = MixtralForCausalLM.from_pretrained("mistralai/Mixtral-8x7B-Instruct-v0.1", device_map="auto")
+tokenizer = MistralTokenizerFast.from_pretrained("mistralai/Mixtral-8x7B-Instruct-v0.1")
+
+messages = [
+    {"role": "user", "content": "What is your favourite condiment?"},
+    {"role": "assistant", "content": "Well, I'm quite partial to a good squeeze of fresh lemon juice. It adds just the right amount of zesty flavour to whatever I'm cooking up in the kitchen!"},
+    {"role": "user", "content": "Do you have mayonnaise recipes?"}
+]
+
+model_inputs = tokenizer.apply_chat_template(messages, return_tensors="pt").to("cuda")
+
+generated_ids = model.generate(model_inputs, max_new_tokens=100, do_sample=True)
+tokenizer.batch_decode(generated_ids)[0]
+################################  end of language examples ##############################
+